YOLO-ED: An efficient lung cancer detection model based on improved YOLOv8 Article Swipe
Qingqiang Zeng
,
Tao Hu
,
Zijie Chen
,
Jieyun Zheng
,
Jianqing Li
,
Yuanyuan Pan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0330732
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0330732
In recent years, You Only Look Once (YOLO) models have gradually been applied to medical image object detection tasks due to their good scalability and excellent generalization performance, bringing new perspectives and approaches to this field. However, existing models overlook the impact of numerous consecutive convolutions and the sampling blur caused by bilinear interpolation, resulting in excessive computational costs and insufficient precision in object detection. To address these problems, we propose a YOLOv8-based model using Efficient modulation and dynamic upsampling (YOLO-ED) to detect lung cancer in CT images. Specifically, we incorporate two innovative modules, the Efficient Modulation module and the DySample module, into the YOLOv8 model. The Efficient Modulation module employs a weighted fusion strategy to extract features from input CT images, effectively reducing model parameters and computational overhead. Furthermore, the DySample module is designed to replace the conventional upsampling component in YOLO, thereby mitigating information loss when expanding feature maps. The dynamic bilinear interpolation introduced by this module increases random bias, which helps minimize errors in feature extraction. To validate the effectiveness of YOLO-ED, we compared it with baselines on the LUNG-PET-CT-DX lung cancer diagnosis dataset and the LUNA16 lung nodule dataset. The results show that YOLO-ED significantly improves precision and reduces computational cost on these two datasets, demonstrating its superiority in the detection of medical images.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0330732
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0330732&type=printable
- OA Status
- gold
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4414005503
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4414005503Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0330732Digital Object Identifier
- Title
-
YOLO-ED: An efficient lung cancer detection model based on improved YOLOv8Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-09-04Full publication date if available
- Authors
-
Qingqiang Zeng, Tao Hu, Zijie Chen, Jieyun Zheng, Jianqing Li, Yuanyuan PanList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0330732Publisher landing page
- PDF URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0330732&type=printableDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0330732&type=printableDirect OA link when available
- Concepts
-
Upsampling, Computer science, Artificial intelligence, Bilinear interpolation, Interpolation (computer graphics), Scalability, Feature (linguistics), Object detection, Subpixel rendering, Computer vision, Pattern recognition (psychology), Image (mathematics), Pixel, Philosophy, Linguistics, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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